Python for Data & Career Growth: Beginner to Advanced

About Course

This 10-week live course is designed to take learners from absolute beginner to confident Python user, with a strong focus on real-world data applications.

Course Content

Week 1 – Introduction to Python & Setup

What is Python and why it matters today
Installing Python, Anaconda, and Jupyter Notebook
Introduction to Google Colab
Writing your first Python program
Understanding variables and data types
Basic input/output operations

Week 2 – Core Programming Fundamentals

Week 3 – Conditions and Decision Making

Week 4 – Loops and Iteration

Week 5 – Data Structures in Python

Week 6 – Functions and Modular Programming

Week 7 – Introduction to Data Analysis (NumPy & Pandas)

Week 8 – Data Visualization (Matplotlib & Seaborn)

Week 9 – Introduction to Machine Learning (Scikit-learn)

Week 10 – Capstone Project & Industry Applications

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Python for Data & Career Growth: Beginner to Advanced

About Course

What Will You Learn?

Course Content

Week 1 – Introduction to Python & Setup

What is Python and why it matters today

Installing Python, Anaconda, and Jupyter Notebook

Introduction to Google Colab

Writing your first Python program

Understanding variables and data types

Basic input/output operations

Week 2 – Core Programming Fundamentals

Data types: integers, floats, strings, booleans

Operators and expressions

Type conversion and casting

Writing clean and readable code

Debugging basics

Introduction to simple problem-solving

Week 3 – Conditions and Decision Making

If, elif, and else statements

Logical operators (and, or, not)

Comparison operators

Nested conditions

Real-life decision-making scenarios

Mini exercises using business logic

Week 4 – Loops and Iteration

For loops and while loops

Iterating through lists and strings

Nested loops

Break and continue statements

Practical use cases (customer data, transactions)

Problem-solving using loops

Week 5 – Data Structures in Python

Lists and list operations

Dictionaries and key-value pairs

Tuples and sets

Data manipulation techniques

Real-world data representation

Practice exercises with structured data

Week 6 – Functions and Modular Programming

Defining and calling functions

Parameters and return values

Writing reusable code

Lambda functions

Organizing code into modules

Practical examples for automation

Week 7 – Introduction to Data Analysis (NumPy & Pandas)

Introduction to NumPy arrays

Working with datasets in Pandas

DataFrames and Series

Data cleaning and preprocessing

Filtering and sorting data

Basic data exploration

Week 8 – Data Visualization (Matplotlib & Seaborn)

Importance of data visualization

Creating line charts, bar charts, and histograms

Customizing plots

Visual storytelling with data

Real-world business dashboards

Interpreting insights from graphs

Week 9 – Introduction to Machine Learning (Scikit-learn)

What is machine learning?

Types of machine learning

Simple regression and classification

Training and testing models

Evaluating model performance

Real-world use cases

Week 10 – Capstone Project & Industry Applications

End-to-end data analysis project

Working with real-world datasets

Applying Python for decision-making

Presentation of insights

Career pathways in data and analytics

Final feedback and next steps

Student Ratings & Reviews

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